Reducing congestion on the highways has been a goal for many years. One possible solution is to make existing highways more efficient through automation. To be safe and effective, however, automated highways require means for positioning vehicles within lanes as well as maintaining optimum distance between vehicles. Therefore, fully automated highway systems require sensor and data processing systems to detect and control the positions of moving vehicles.
Positioning vehicles on an automated highway, such as the proposed Intelligent Vehicle Highway System (IVHS), is complicated by the clutter of unwanted information from the environment that is continually received by the sensor system. Provisions must be made for system calibration, changing weather, vehicles entering and exiting the highway, and numerous other obstacles that might be encountered. Various systems have been proposed for automated highways, including those employing active sensors such as mm wave radar, laser radar, or sonar, and passive systems such as stereo vision for measuring distance between vehicles. The known systems, however, have high cost factors and/or technical problems that have not been overcome. For example, a wide field of view is needed for lane detection, and a highly resolved image with many pixels currently cannot be processed in real time. Given the foregoing constraints and the desire to develop automated highways, there is a need for a safe, effective, low cost, real time system for sensing and controlling the position of automotive vehicles in lanes of present highways and automated highways of the future.